SUMMARY Recent technological developments such as tissue clearing and light sheet imaging have allowed for the three- dimensional visualization of the entire brain at cellular resolution. Translucence Biosystems has advanced this technology further by developing a) a proprietary Mesoscale Imaging System that allows visualization of an entire mouse brain much faster than prior techniques, b) machine learning-based algorithms that identify individual cells across the entire intact mouse brain and c) routines that rapidly determine cell densities in >1,200 brain regions defined by the annotated Allen Brain Atlas. In the present proposal we will show the power of our technique by visualizing microglia density across the entire mouse brain as a biological marker for neuroinflammation. Microglia are the resident immune cells in the brain. While they play important roles in healthy brain function, they also mediate neuroinflammatory processes that have a significant impact in multiple neurodegenerative diseases such as Alzheimer's, Parkinson's, and multiple sclerosis as well as play possible roles in several neurodevelopmental and neurological disorders (e.g., schizophrenia, autism spectrum disorder, chemo brain). A tool for three-dimensional imaging of neuroinflammation patterns across the whole brain will help advance our understanding of neuroinflammatory processes in neurological diseases and aid in the evaluation of therapeutic approaches. To visualize microglia, we will evaluate multiple strategies, including the CX3CR1-GFP mouse line, which expresses GFP in microglia, Iba1 immunolabeling and other antibody markers of activated microglia. The utility of our approach for monitoring neuroinflammation will be demonstrated in CX3CR1-GFP mice by treating them with the inflammatory agent lipopolysaccharide (LPS). Mice will be injected with three different LPS doses, and then microglia will be visualized and automatically counted across >1,200 brain regions. We will confirm the neuroinflammatory response to LPS by measuring protein markers of inflammation. The applicability of our technique to neurodegenerative disease will be demonstrated by studying a mouse line used as a model of Alzheimer's disease that presents pronounced patterns of neuroinflammation. These experiments are designed to prove that our approach can provide reliable and detailed information describing neuroinflammation and that our results are better in terms of speed, resolution, and richness of information than any other technique available. Once the goals for Phase I are met, we will be positioned to develop our microglia activation assay into a new gold standard for precise and complete histological detection of neuroinflammation. During phase II, we plan to 1) develop machine learning tools to establish morphological criteria that differentiate resting from activated microglia, 2) characterize microglial signatures in various mouse models of diseases with known or suspected neuroinflammation components, and 3) validate the microglial targeting of anti-inflammatory therapeutic candidates.